Machine learning models often require large datasets (e.g., many hundreds or thousands of entries) to train machine learning models for a specific application. The content of such datasets may include data in different orders and/or sequences (e.g., ascending order, descending order, numerical series, etc.). These datasets may be sampled randomly and provided as input to the machine learning models. In addition, the sampled data may be shuffled before being input to the machine learning models. This random sampling and shuffling suppresses the original sequence of the data, thus making the data less useful as a learning set. At times, a portion of these datasets may include ordered sequence, and to identify this ordered sequence from the datasets is not possible. Consequently, there is a need to automatically identify ordered sequence data in machine learning datasets in order to make more effective and efficient prediction of the content within a dataset.